Conventional statistics software and conventional neural network software identify input-output relationships during a training phase, and each apply the learned input-output relationships during a performance phase. For example, during the training phase a neural network adjusts connection weights until known target output values are produced from known input values. During the performance phase, the neural network uses connection weights identified during the training phase to impute unknown output values from known input values.
A conventional neural network consists of simple interconnected processing elements. The basic operation of each processing element is the transformation of its input signals to a useful output signal. Each interconnection transmits signals from one element to another element, with a relative effect on the output signal that depends oil the weight for the particular interconnection. A conventional neural network may be trained by providing known input values and output values to the network, which causes the interconnection weights to be changed.
A variety of conventional neural network learning methods and models have been developed for massively parallel processing. Among these methods and models, back propagation is the most widely used learning method and the multi-layer perceptron is the most widely used model. Multi-layer perceptrons have two or more processing element layers, most commonly an input layer, a single hidden layer and an output layer. The hidden layer contains processing elements that enable conventional neural networks to identify nonlinear input-output relationships.
Conventional neural network learning and performing operations can be performed quickly during each respective stage, because neural network processing elements can perform in parallel. Conventional neural network accuracy depends on data predictability and network structure that are pre-specified by the user, including the number of layers and the number of processing elements in each layer.
Conventional neural network learning occurs when a set of training records is imposed on the network, with each such record containing fixed input and output values. The network uses each record to update the network's learning by first computing network outputs as a function of the record inputs along with connection weights and other parameters that have been learned up to that point. The weights are then adjusted depending on the closeness of the computed output values to the training record output values. For example, suppose that a trained output value is 1.0 and the network computed value is 0.4. The network error will be 0.6 (1.0-0.4=0.6), which will be used to determine the weight adjustments necessary for minimizing the error. Training occurs by adjusting weights in the same way until all such training records have been used, after which the process is repeated until all error values have been sufficiently reduced.
Conventional neural network training and performance phases differ in two basic ways. While weight values change during training to decrease errors between training and computed outputs, weight values are fixed during the performance phase. Additionally, output values are known during the training phase, but output values can only be predicted during the performance phase. The predicted output values are a function of performance phase input values and connection weight values that were learned during the training phase.
While input-output relationship identification through conventional statistical analysis and neural network analysis may be satisfactory for some applications, both Such approaches have limited utility in other applications. Effective manual data analysis requires extensive training and experience, along with time-consuming effort. Conventional neural network analysis requires less training and effort, although the results produced by conventional neural networks are less reliable and harder to interpret than manual results.
A deficiency of both conventional statistics methods and conventional neural network methods results from the distinct training and performance phases implemented by each method. Requiring two distinct phases causes considerable learning time to be spent before performance can begin. Training delays occur in manual statistics methods because even trained expert analysis takes considerable time, and training delays occur in neural network methods because many training passes through numerous training records are needed. Thus, conventional statistical analysis is limited to settings where (a) delays are acceptable between the time learning occurs and the time learned models are used, and (b) input-output relationships are stable between the time training analysis begins and performance operations begin.
Thus, there is a need in the art for an information processing system that may operate quickly to either learn or perform or both within a time trial.